针对现有的工业控制系统异常检测分类方法大多无法有效处理类不平衡和重叠耦合的问题,提出了一种基于干扰样本分布优化的工控异常检测改进SVM模型(Improved SVM Model Based on Adaptive Differential Evolution with Sphere, SJADE_SV...针对现有的工业控制系统异常检测分类方法大多无法有效处理类不平衡和重叠耦合的问题,提出了一种基于干扰样本分布优化的工控异常检测改进SVM模型(Improved SVM Model Based on Adaptive Differential Evolution with Sphere, SJADE_SVM),该模型将基于超球体覆盖的自适应差分进化过采样技术与支持向量机相结合。首先,通过改进超球体覆盖算法和构建概率公式,来识别和排除干扰样本;然后,改进合成少数派过采样技术,通过对安全样本采样,缓解类不平衡和重叠耦合问题;最后,使用自适应差分进化算法优化样本的位置和属性,同时使用SVM进行分类。在6个真实工控数据集和4个UCI公开数据集上共设计3组实验,包括与逻辑回归和高斯朴素贝叶斯等异常检测分类算法的性能对比、改善样本分布方法的实验对比以及算法的运行时间对比。实验结果表明,该模型在F-score和G-mean评价指标上分别提高了38.29%和10.54%,分类效果稳居前三,且在α=0.05的非参数双侧Wilcoxon符号秩检验和Friedman检验等统计实验中表现出显著的性能优势。展开更多
Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statisti...Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.展开更多
文摘针对现有的工业控制系统异常检测分类方法大多无法有效处理类不平衡和重叠耦合的问题,提出了一种基于干扰样本分布优化的工控异常检测改进SVM模型(Improved SVM Model Based on Adaptive Differential Evolution with Sphere, SJADE_SVM),该模型将基于超球体覆盖的自适应差分进化过采样技术与支持向量机相结合。首先,通过改进超球体覆盖算法和构建概率公式,来识别和排除干扰样本;然后,改进合成少数派过采样技术,通过对安全样本采样,缓解类不平衡和重叠耦合问题;最后,使用自适应差分进化算法优化样本的位置和属性,同时使用SVM进行分类。在6个真实工控数据集和4个UCI公开数据集上共设计3组实验,包括与逻辑回归和高斯朴素贝叶斯等异常检测分类算法的性能对比、改善样本分布方法的实验对比以及算法的运行时间对比。实验结果表明,该模型在F-score和G-mean评价指标上分别提高了38.29%和10.54%,分类效果稳居前三,且在α=0.05的非参数双侧Wilcoxon符号秩检验和Friedman检验等统计实验中表现出显著的性能优势。
文摘Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.
